Early driver intention prediction plays a significant role in intelligent vehicles. Drivers exhibit various driving characteristics impairing the performance of
conventional algorithms using all drivers’ data indiscriminatingly. This paper develops a personalized driver intention prediction system at unsignalized T-intersections by
seamlessly integrating clustering and classification. Polynomial regression mixture (PRM) clustering and Akaike’s
Information Criterion are applied to individual drivers trajectories for learning in-depth driving behaviours. Then
various classifiers are evaluated to link low-level vehicle
states to high-level driving behaviours. CART classifier
with Bayesian optimization excels others in accuracy and
computation. The proposed system is validated by a realworld driving dataset. Comparative experimental results
indicate that PRM clustering can discover more in-depth
driving behaviours than manually defined manoeuvres due
to its fine ability in accounting for both spatial and temporal information; the proposed framework integrating PRM
clustering and CART classification provides promising intention prediction performance and is adaptive to different
drivers.
Funding
This work is jointly supported by the U.K.
Engineering and Physical Sciences Research Council (EPSRC) Autonomous and Intelligent Systems programme under the grant number
EP/J011525/1 with BAE Systems as the leading industrial partner.
History
School
Aeronautical, Automotive, Chemical and Materials Engineering
Department
Aeronautical and Automotive Engineering
Published in
IEEE Transactions on Industrial Informatics
Volume
15
Issue
6
Pages
3693 - 3702
Citation
YI, D. ... et al., 2018. Trajectory clustering aided personalized driver intention prediction for intelligent vehicles. IEEE Transactions on Industrial Informatics, 15 (6), pp.3693-3702.